Integrating Generative AI into Live Case Studies for Experiential Learning in Operations Management

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Your manuscript is very timely and addresses an important gap in the literature of GenAI applications in higher education. The integrative framework you present for supporting experiential learning through mapping of GenAI tools across Kolb’s ELC learning stages in the context of live cases is both conceptually novel and practically valuable. You have also demonstrated scholarly integrity in your discussion of the study’s limitations and appropriate methodological choices for an exploratory investigation like this.

However, there are several issues across multiple dimensions (methodological, analytical, etc.) that should be addressed:

1. Methodological concerns (response rates, attribution problem, high failure rate), and
2. The results organization and overall writing quality need improvement

The theoretical contribution to existing scholarship is important but in need of sharper articulation of what is novel beyond a combination of existing elements (live cases, experiential learning theory, GenAI). The study has strong potential, but major changes are needed to strengthen the empirical basis, clarify the contribution, and provide a more critical analysis of GenAI integration success/failure to extract pedagogical lessons from this first case study.

Major Revisions Needed:

1. Research Question and Objectives

The research question is never explicitly stated in a stand-alone format, which is critical for research transparency. It is only implied throughout the paper without direct articulation.

Recommendation: Add an RQ to the Introduction (lines 40-50), which is clearly focused and articulates your central inquiry, such as “How can GenAI be integrated into live case studies using Kolb’s ELC to enhance experiential learning in higher education operations management?”

2. Methodological Concerns

Low response rates for the live case-specific survey.

The 19% response rate for the live case-specific survey severely weakens the validity of your claims and should be addressed more critically.

Recommendation: In the limitations section (lines 718-759), you should explicitly discuss how the 19% response rate may have introduced selection bias (more engaged students more likely to respond) and how this limitation impacts the interpretation of their positive findings in section 4.1-4.4. The low response rate weakens any claims made about the specific pedagogical impact of GenAI integration.

Attribution problem: inability to isolate GenAI impact.

Cannot attribute learning gains to (a) GenAI, (b) the live case design, or (c) the interaction between GenAI and the live case, yet this study design limitation doesn’t seem to be acknowledged.

Recommendation: Soften all the claims about the contribution of GenAI to positive learning outcomes and avoid the parts of the Discussion that overstate what the data can support, e.g. lines 614-621. Add a sentence to the Discussion of methodological limitations in Table 2, e.g. “Future research should use comparative designs (live cases with/without GenAI integration) to isolate the pedagogical contribution of GenAI tools”.

Failure rate

The high failure rate of 28% (lines 311-320) is very concerning and may be a sign of pedagogical design issues beyond student engagement. Need to more deeply analyze the causes of this high failure rate and whether GenAI literacy, attendance, and other factors were contributors before making the claims about GenAI’s effectiveness that they make in the Discussion.

Recommendation: Conduct an analysis of whether failures were distributed unevenly among students with higher/lower GenAI literacy, attendance issues, or other factors and report/analyze this data. If this data is not available, you should acknowledge that this is a limitation to interpreting their findings.

3. Results Organization

 The Results section 4.1-4.4 has significant redundancy and could be streamlined.

• Lines 425-453: Description of how GenAI was used in each ELC stage of the live case, while providing necessary context, is somewhat repetitive of Table 1 and Figure 3
• Lines 459-504: The business model proposals are interesting and some of the most engaging aspects of the study, but this level of detail is much more than is needed to answer the research question.

Recommendation: Consolidate sections 4.2 and 4.3, i.e. “Integration of GenAI in the ELC” and “Student Outputs”, into a single section where the focus is on the aspects of student outputs that directly relate to and answer the research question of how GenAI was used to support experiential learning, including the student experience with GenAI. You should move all the long-form content of detailed business model descriptions to supplementary materials or condense this part to 1-2 illustrative examples and move the majority of the detailed student outputs to Appendix 1 or 2. The focus in 4.1-4.4 should be on evidence directly related to GenAI’s pedagogical impact, not the full range of student work.

4. Critical Analysis of GenAI’s Role and Effectiveness

Current weakness: The paper has nothing in the Results/Discussion about when GenAI was actively unhelpful for student learning or what specific types of prompts/uses were most or least effective.    

Recommendations:

• Add a subsection in Results/Discussion to provide analysis of "differential effectiveness" of GenAI use, e.g. lines 584-593 allude to this but don’t develop the argument
• Include examples of bad or failed uses of GenAI in student work (if available from the 19% response rate)
• Expand on the “GenAI literacy” gap with specific evidence from student outputs

5. Clarity of Theoretical Contribution

• Lines 660-673: More clearly articulate the specific contribution their work is making to theoretical understanding of GenAI in learning.

Current: “This synthesis advances the theoretical discourse on smart education...”

Problem: Too broad and vague to be clear.

Recommendation: Be specific about what is being theoretically advanced that is novel.

“This study extends experiential learning theory by showing how AI tools can serve as ‘cognitive partners’ (lines 38-40) at all four ELC stages, while prior work has conceptualized AI as only content generation (lines 38-40). We provide a structured pedagogical model for technology-enhanced experiential learning by mapping specific GenAI affordances to each of the four ELC stages (CE: contextual data; RO: probing questions; AC: framework generation; AE: safe testing), and showing how these affordances map onto key actions/functions of the learning process.” 

Minor Revisions Recommended:

6. Introduction

• Lines 106-112: The paragraph here about smart education and technology effectiveness is a bit out of place and disconnected from the rest of the discussion. Either work it into the flow of the paper more (develop the smart education framework more fully) or cut this brief mention and discussion.
• Lines 135-142: The related work paragraph about limitations of GenAI for OMS education is good but also not well connected to your study design decisions (why you didn’t choose to apply ChatGPT/Alpha/other tools directly to live case or business model design/analysis for example).

7. Literature Review Structure

Recommend reorganizing/rewording Sections 1.1-1.4 with clearer signposting, as the current numbered subheadings are confusing.

Add descriptive subheadings to each section to give a clearer sense of the argument being made in each section.

1. 1.1 “GenAI in Higher Education: Promises and Pitfalls”
2. 1.2 “Live Case Studies as Authentic Learning Contexts”
3. 1.3 “Experiential Learning: Kolb’s ELC Framework”
4. 1.4 “Student Agency in GenAI-Enhanced Learning”

Author Response

Please see the attachement

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript presents an original didactic methodology that integrates generative artificial intelligence (GenAI) tools with case study analysis within an Operations Management module. The incorporation of GenAI into teaching is a rapidly developing area and undoubtedly contributes to better preparing students to use these technologies in their future professional careers. The methodology presented in the article is innovative, clearly described, and well grounded in the existing literature. The results are presented in a transparent manner and are subjected to a thorough discussion. A particularly interesting and valuable contribution is the integration of GenAI into Kolb’s learning cycle.

However, the article has several limitations. First, the sample size is relatively small (86 participants) and consists solely of students enrolled in a single course at the same university during the same semester. Additionally, all participants analyzed the same case study, which limits the generalizability of the findings. The authors acknowledge this weakness and discuss it in subsection 5.5. Second, the response rate in the surveys was low—46 out of 86 students completed the first survey, and only 16 participated in the second one. This significantly reduces the robustness and interpretative strength of the results.

Despite these limitations, I believe that the manuscript provides meaningful value to the existing literature and deserves publication in Education Sciences. Below, I offer several minor comments that could further improve the quality of the article:

1. In the Subsection 4.2.1 the manuscript refers to group work, but it does not describe how the groups were formed. The authors should clarify the criteria for group allocation, group sizes, and how participants were assigned. This information is important for interpreting the results.
2. The citation style used in the manuscript does not follow the journal’s guidelines. Most citations are numbered, whereas line 124 includes a citation in the format “(Belkina et al. 2025),” which aligns with the Education Sciences requirements. The ordering of references in the References section is also inconsistent with the journal’s formatting rules. The entire bibliography needs to be standardized.

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Student Quote Attribution:

The student quotes (lines 480-481, 485-486, 490-492, 496-497) are now properly formatted and attributed. One minor suggestion that would be helpful is to clarify whether these are from survey responses or written assignments (it appears they are written assignments from the context, but it would be nice to state that explicitly).

Author Response

Dear Reviewer,

We thank you for this helpful clarification request. As suggested, we have now made explicit that the student quotes come from their written assignments. Section 4.2 (line 486 in the new document version) has been updated to read: “The deductive theme analysis from the students’ written assignments provided the following ELC descriptions.” We appreciate the reviewer’s attention to detail, which has improved the clarity of the manuscript.